v0.59.0
Browse filesSee https://github.com/qualcomm/ai-hub-models/releases/v0.59.0 for changelog.
- README.md +102 -105
- release_assets.json +15 -17
README.md
CHANGED
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@@ -17,7 +17,7 @@ pipeline_tag: image-segmentation
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UNet is a machine learning model that produces a segmentation mask for an image. The most basic use case will label each pixel in the image as being in the foreground or the background. More advanced usage will assign a class label to each pixel. This version of the model was trained on the data from Kaggle's Carvana Image Masking Challenge (see https://www.kaggle.com/c/carvana-image-masking-challenge) and is used for vehicle segmentation.
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This is based on the implementation of Unet-Segmentation found [here](https://github.com/milesial/Pytorch-UNet).
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This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.
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Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.
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| ONNX | w8a8 | Universal | QAIRT 2.45, ONNX Runtime 1.
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| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
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| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
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| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
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| TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
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For more device-specific assets and performance metrics, visit **[Unet-Segmentation on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/unet_segmentation)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.
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- Custom weights (e.g., fine-tuned checkpoints)
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- Custom input shapes
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- Target device and runtime configurations
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This option is ideal if you need to customize the model beyond the default configuration provided here.
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See our repository for [Unet-Segmentation on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.
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## Model Details
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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|---|---|---|---|---|---|---
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| Unet-Segmentation | ONNX | float | Snapdragon® X2 Elite | 74.
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| Unet-Segmentation | ONNX | float | Snapdragon® X Elite | 142.
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| Unet-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile |
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| Unet-Segmentation | ONNX | float | Snapdragon® 8 Gen 1 Mobile |
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| Unet-Segmentation | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) |
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| Unet-Segmentation | ONNX | float | Qualcomm® QCS8450 |
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| Unet-Segmentation | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 |
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| Unet-Segmentation | ONNX | float |
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| Unet-Segmentation | ONNX | float |
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| Unet-Segmentation | ONNX | float |
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| Unet-Segmentation | ONNX | float |
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X2 Elite | 18.
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X Elite | 37.
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 29.
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Gen 1 Mobile |
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 299.
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 38.
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS8450 |
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| Unet-Segmentation | ONNX | w8a8 |
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| Unet-Segmentation | ONNX | w8a8 |
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| Unet-Segmentation | ONNX | w8a8 |
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| Unet-Segmentation | ONNX | w8a8 |
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| Unet-Segmentation | ONNX | w8a8 |
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| Unet-Segmentation |
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| Unet-Segmentation |
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| Unet-Segmentation |
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| Unet-Segmentation | QNN_DLC | float | Snapdragon®
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| Unet-Segmentation | QNN_DLC | float |
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| Unet-Segmentation | QNN_DLC | float |
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| Unet-Segmentation | QNN_DLC | float |
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| Unet-Segmentation | QNN_DLC | float | Qualcomm®
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| Unet-Segmentation | QNN_DLC | float | Qualcomm®
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| Unet-Segmentation | QNN_DLC | float | Qualcomm®
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| Unet-Segmentation | QNN_DLC | float | Qualcomm®
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| Unet-Segmentation | QNN_DLC | float | Qualcomm®
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| Unet-Segmentation | QNN_DLC | float | Qualcomm®
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| Unet-Segmentation | QNN_DLC | float | Qualcomm®
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| Unet-Segmentation | QNN_DLC | float |
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| Unet-Segmentation | QNN_DLC | float |
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| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite Mobile |
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| Unet-Segmentation | QNN_DLC |
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| Unet-Segmentation | QNN_DLC |
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| Unet-Segmentation | QNN_DLC |
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| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon®
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm®
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation | QNN_DLC | w8a8 |
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| Unet-Segmentation |
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| Unet-Segmentation |
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| Unet-Segmentation |
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| Unet-Segmentation | TFLITE | float |
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| Unet-Segmentation | TFLITE | float |
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| Unet-Segmentation | TFLITE | float | Qualcomm®
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| Unet-Segmentation | TFLITE | float | Qualcomm®
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| Unet-Segmentation | TFLITE | float | Qualcomm®
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| Unet-Segmentation | TFLITE | float | Qualcomm®
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| Unet-Segmentation | TFLITE | float | Qualcomm®
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| Unet-Segmentation | TFLITE | float | Qualcomm®
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| Unet-Segmentation | TFLITE | float | Qualcomm®
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| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite
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| Unet-Segmentation | TFLITE | float |
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| Unet-Segmentation | TFLITE |
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| Unet-Segmentation | TFLITE |
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| Unet-Segmentation | TFLITE |
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm®
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm®
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm®
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm®
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm®
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm®
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm®
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 |
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8295P | 63.758 ms | 2 - 180 MB | NPU
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 78.944 ms | 1 - 264 MB | NPU
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| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 22.028 ms | 2 - 188 MB | NPU
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## License
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* The license for the original implementation of Unet-Segmentation can be found
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UNet is a machine learning model that produces a segmentation mask for an image. The most basic use case will label each pixel in the image as being in the foreground or the background. More advanced usage will assign a class label to each pixel. This version of the model was trained on the data from Kaggle's Carvana Image Masking Challenge (see https://www.kaggle.com/c/carvana-image-masking-challenge) and is used for vehicle segmentation.
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This is based on the implementation of Unet-Segmentation found [here](https://github.com/milesial/Pytorch-UNet).
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+
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/unet_segmentation) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-onnx-float.zip)
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| ONNX | w8a8 | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-onnx-w8a8.zip)
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| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-qnn_dlc-float.zip)
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| QNN_DLC | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-qnn_dlc-w8a8.zip)
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| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[Unet-Segmentation on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/unet_segmentation)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/unet_segmentation) Python library to compile and export the model with your own:
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- Custom weights (e.g., fine-tuned checkpoints)
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- Custom input shapes
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- Target device and runtime configurations
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This option is ideal if you need to customize the model beyond the default configuration provided here.
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+
See our repository for [Unet-Segmentation on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/unet_segmentation) for usage instructions.
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## Model Details
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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|---|---|---|---|---|---|---
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| Unet-Segmentation | ONNX | float | Snapdragon® X2 Elite | 74.926 ms | 17 - 17 MB | NPU
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| Unet-Segmentation | ONNX | float | Snapdragon® X Elite | 142.497 ms | 54 - 54 MB | NPU
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| Unet-Segmentation | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 114.579 ms | 23 - 546 MB | NPU
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| Unet-Segmentation | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 289.805 ms | 1 - 549 MB | NPU
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| Unet-Segmentation | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 159.65 ms | 0 - 57 MB | NPU
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| Unet-Segmentation | ONNX | float | Qualcomm® QCS8450 | 289.805 ms | 1 - 549 MB | NPU
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| Unet-Segmentation | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 249.85 ms | 9 - 21 MB | NPU
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| Unet-Segmentation | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 142.497 ms | 54 - 54 MB | NPU
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| Unet-Segmentation | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 91.522 ms | 15 - 334 MB | NPU
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| Unet-Segmentation | ONNX | float | Snapdragon® 8 Elite Mobile | 91.522 ms | 15 - 334 MB | NPU
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| Unet-Segmentation | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 66.985 ms | 14 - 343 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X2 Elite | 18.89 ms | 5 - 5 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® X Elite | 37.719 ms | 29 - 29 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 29.764 ms | 6 - 340 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Gen 1 Mobile | 68.38 ms | 6 - 342 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 299.862 ms | 3 - 8 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 38.042 ms | 0 - 32 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® QCS8450 | 68.38 ms | 6 - 342 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 35.708 ms | 4 - 7 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® Dragonwing™ IQ-X7181 | 37.719 ms | 29 - 29 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 24.393 ms | 3 - 189 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Elite Mobile | 24.393 ms | 3 - 189 MB | NPU
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| Unet-Segmentation | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 16.406 ms | 3 - 192 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Snapdragon® X2 Elite | 71.644 ms | 9 - 9 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Snapdragon® X Elite | 132.325 ms | 9 - 9 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 102.166 ms | 139 - 655 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 283.41 ms | 5 - 540 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8275 | 953.564 ms | 1 - 324 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 138.073 ms | 9 - 13 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8775P | 240.435 ms | 2 - 325 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8650P | 240.435 ms | 2 - 325 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8255P | 240.435 ms | 2 - 325 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® QCS8450 | 283.41 ms | 5 - 540 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 239.046 ms | 9 - 27 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 132.325 ms | 9 - 9 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 81.754 ms | 0 - 332 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA7255P | 953.564 ms | 1 - 324 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Qualcomm® SA8295P | 274.424 ms | 0 - 322 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 81.754 ms | 0 - 332 MB | NPU
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| Unet-Segmentation | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 61.146 ms | 0 - 345 MB | NPU
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| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® X2 Elite | 18.842 ms | 2 - 2 MB | NPU
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| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® X Elite | 35.723 ms | 2 - 2 MB | NPU
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| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 26.201 ms | 2 - 320 MB | NPU
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| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Gen 1 Mobile | 58.741 ms | 2 - 318 MB | NPU
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 287.899 ms | 4 - 9 MB | NPU
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| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS8275 | 121.487 ms | 1 - 180 MB | NPU
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| 115 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 34.563 ms | 3 - 31 MB | NPU
|
| 116 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8775P | 32.187 ms | 1 - 181 MB | NPU
|
| 117 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8650P | 32.187 ms | 1 - 181 MB | NPU
|
| 118 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8255P | 32.187 ms | 1 - 181 MB | NPU
|
| 119 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® QCS8450 | 58.741 ms | 2 - 318 MB | NPU
|
| 120 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 32.544 ms | 1 - 7 MB | NPU
|
| 121 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ IQ-X7181 | 35.723 ms | 2 - 2 MB | NPU
|
| 122 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-6690 | 1233.117 ms | 31 - 551 MB | NPU
|
| 123 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 79.054 ms | 2 - 267 MB | NPU
|
| 124 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 21.707 ms | 2 - 188 MB | NPU
|
| 125 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA7255P | 121.487 ms | 1 - 180 MB | NPU
|
| 126 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Qualcomm® SA8295P | 63.726 ms | 0 - 180 MB | NPU
|
| 127 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Elite Mobile | 21.707 ms | 2 - 188 MB | NPU
|
| 128 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 15.858 ms | 2 - 200 MB | NPU
|
| 129 |
+
| Unet-Segmentation | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 79.054 ms | 2 - 267 MB | NPU
|
| 130 |
+
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 104.617 ms | 6 - 576 MB | NPU
|
| 131 |
+
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 278.817 ms | 7 - 589 MB | NPU
|
| 132 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® Dragonwing™ QCS8275 | 953.52 ms | 0 - 324 MB | NPU
|
| 133 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 144.104 ms | 6 - 443 MB | NPU
|
| 134 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8775P | 240.471 ms | 7 - 330 MB | NPU
|
| 135 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8650P | 240.471 ms | 7 - 330 MB | NPU
|
| 136 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8255P | 240.471 ms | 7 - 330 MB | NPU
|
| 137 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® QCS8450 | 278.817 ms | 7 - 589 MB | NPU
|
| 138 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 239.448 ms | 6 - 86 MB | NPU
|
| 139 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 82.942 ms | 0 - 332 MB | NPU
|
| 140 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® SA7255P | 953.52 ms | 0 - 324 MB | NPU
|
| 141 |
+
| Unet-Segmentation | TFLITE | float | Qualcomm® SA8295P | 274.445 ms | 7 - 329 MB | NPU
|
| 142 |
+
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite Mobile | 82.942 ms | 0 - 332 MB | NPU
|
| 143 |
+
| Unet-Segmentation | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 64.672 ms | 0 - 340 MB | NPU
|
| 144 |
+
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 26.23 ms | 1 - 316 MB | NPU
|
| 145 |
+
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Gen 1 Mobile | 60.694 ms | 2 - 319 MB | NPU
|
| 146 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ QCS6490 | 288.767 ms | 1 - 41 MB | NPU
|
| 147 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ QCS8275 | 121.558 ms | 2 - 181 MB | NPU
|
| 148 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 33.929 ms | 2 - 624 MB | NPU
|
| 149 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8775P | 32.204 ms | 2 - 180 MB | NPU
|
| 150 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8650P | 32.204 ms | 2 - 180 MB | NPU
|
| 151 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8255P | 32.204 ms | 2 - 180 MB | NPU
|
| 152 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® QCS8450 | 60.694 ms | 2 - 319 MB | NPU
|
| 153 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ IQ-9075 | 32.211 ms | 1 - 38 MB | NPU
|
| 154 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-6690 | 1213.903 ms | 0 - 519 MB | NPU
|
| 155 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-7790 | 78.724 ms | 1 - 270 MB | NPU
|
| 156 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® Dragonwing™ Q-8750 | 21.596 ms | 2 - 189 MB | NPU
|
| 157 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA7255P | 121.558 ms | 2 - 181 MB | NPU
|
| 158 |
+
| Unet-Segmentation | TFLITE | w8a8 | Qualcomm® SA8295P | 63.743 ms | 2 - 180 MB | NPU
|
| 159 |
+
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Elite Mobile | 21.596 ms | 2 - 189 MB | NPU
|
| 160 |
+
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 16.061 ms | 1 - 199 MB | NPU
|
| 161 |
+
| Unet-Segmentation | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 78.724 ms | 1 - 270 MB | NPU
|
|
|
|
|
|
|
|
|
|
| 162 |
|
| 163 |
## License
|
| 164 |
* The license for the original implementation of Unet-Segmentation can be found
|
release_assets.json
CHANGED
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{
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"
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
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| 12 |
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|
| 13 |
"qnn_dlc": {
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"tool_versions": {
|
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|
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-
"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
|
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},
|
| 19 |
-
"
|
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"tool_versions": {
|
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"qairt": "2.45.0.260326154327"
|
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"onnx_runtime": "1.25.0"
|
| 23 |
},
|
| 24 |
-
"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
|
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}
|
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}
|
| 27 |
},
|
| 28 |
"w8a8": {
|
| 29 |
"universal_assets": {
|
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"
|
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"tool_versions": {
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"qairt": "2.45.0.260326154327",
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-
"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
|
| 36 |
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|
| 37 |
"qnn_dlc": {
|
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"tool_versions": {
|
| 39 |
"qairt": "2.45.0.260326154327"
|
| 40 |
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| 41 |
-
"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
|
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|
| 43 |
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|
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"tool_versions": {
|
| 45 |
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| 46 |
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|
| 47 |
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| 48 |
-
"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.
|
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}
|
| 50 |
}
|
| 51 |
}
|
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| 1 |
{
|
| 2 |
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"version": "0.59.0",
|
| 3 |
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|
| 4 |
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|
| 5 |
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| 6 |
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|
| 7 |
"tool_versions": {
|
| 8 |
"qairt": "2.45.0.260326154327",
|
| 9 |
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"onnx_runtime": "1.27.1"
|
| 10 |
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|
| 11 |
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|
| 12 |
},
|
| 13 |
"qnn_dlc": {
|
| 14 |
"tool_versions": {
|
| 15 |
"qairt": "2.45.0.260326154327"
|
| 16 |
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|
| 17 |
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-qnn_dlc-float.zip"
|
| 18 |
},
|
| 19 |
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"tflite": {
|
| 20 |
"tool_versions": {
|
| 21 |
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"qairt": "2.45.0.260326154327"
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|
|
|
| 22 |
},
|
| 23 |
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"download_url": "https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/unet_segmentation/releases/v0.59.0/unet_segmentation-tflite-float.zip"
|
| 24 |
}
|
| 25 |
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|
| 26 |
},
|
| 27 |
"w8a8": {
|
| 28 |
"universal_assets": {
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| 29 |
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"onnx": {
|
| 30 |
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|
| 31 |
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|
| 33 |
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| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
"tool_versions": {
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| 38 |
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|
| 39 |
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| 40 |
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|
| 41 |
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| 42 |
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|
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|
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|
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|
| 48 |
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|
| 49 |
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|